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体验新版 GitCode,发现更多精彩内容 >>
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8e3ecf5d
编写于
10月 26, 2017
作者:
C
chengduo
提交者:
GitHub
10月 26, 2017
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差异文件
Merge pull request #4814 from chengduoZH/Add_sequence_project_op
Add sequence_conv_op and sequence_projection functor
上级
9d142d50
99c6f44a
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
858 addition
and
0 deletion
+858
-0
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+2
-0
paddle/operators/math/CMakeLists.txt
paddle/operators/math/CMakeLists.txt
+2
-0
paddle/operators/math/context_project.cc
paddle/operators/math/context_project.cc
+26
-0
paddle/operators/math/context_project.cu
paddle/operators/math/context_project.cu
+28
-0
paddle/operators/math/context_project.h
paddle/operators/math/context_project.h
+231
-0
paddle/operators/sequence_conv_op.cc
paddle/operators/sequence_conv_op.cc
+177
-0
paddle/operators/sequence_conv_op.cu
paddle/operators/sequence_conv_op.cu
+24
-0
paddle/operators/sequence_conv_op.h
paddle/operators/sequence_conv_op.h
+170
-0
python/paddle/v2/framework/tests/test_seq_conv.py
python/paddle/v2/framework/tests/test_seq_conv.py
+198
-0
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
8e3ecf5d
...
...
@@ -123,6 +123,7 @@ set(DEPS_OPS
sum_op
pool_op
pool_with_index_op
sequence_conv_op
lstm_op
)
...
...
@@ -134,6 +135,7 @@ op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
op_library
(
sum_op DEPS net_op
)
op_library
(
pool_op DEPS pooling
)
op_library
(
pool_with_index_op DEPS pooling
)
op_library
(
sequence_conv_op DEPS context_project
)
op_library
(
lstm_op DEPS sequence2batch lstm_compute
)
list
(
REMOVE_ITEM GENERAL_OPS
${
DEPS_OPS
}
)
...
...
paddle/operators/math/CMakeLists.txt
浏览文件 @
8e3ecf5d
...
...
@@ -9,6 +9,7 @@ if(WITH_GPU)
nv_library
(
cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator
)
nv_library
(
pooling SRCS pooling.cc pooling.cu DEPS device_context
)
nv_library
(
vol2col SRCS vol2col.cc vol2col.cu DEPS device_context
)
nv_library
(
context_project SRCS context_project.cc context_project.cu DEPS device_context
)
nv_library
(
sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context
)
nv_library
(
lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions
)
else
()
...
...
@@ -18,6 +19,7 @@ else()
cc_library
(
cross_entropy SRCS cross_entropy.cc DEPS operator
)
cc_library
(
pooling SRCS pooling.cc DEPS device_context
)
cc_library
(
vol2col SRCS vol2col.cc DEPS device_context
)
cc_library
(
context_project SRCS context_project.cc DEPS device_context
)
cc_library
(
sequence2batch SRCS sequence2batch.cc DEPS device_context
)
cc_library
(
lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions
)
endif
()
...
...
paddle/operators/math/context_project.cc
0 → 100644
浏览文件 @
8e3ecf5d
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/math/context_project.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
class
ContextProjectFunctor
<
platform
::
CPUPlace
,
float
>;
template
class
ContextProjectFunctor
<
platform
::
CPUPlace
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/context_project.cu
0 → 100644
浏览文件 @
8e3ecf5d
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/math/context_project.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
class
ContextProjectFunctor
<
platform
::
GPUPlace
,
float
>;
template
class
ContextProjectFunctor
<
platform
::
GPUPlace
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/context_project.h
0 → 100644
浏览文件 @
8e3ecf5d
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/math/im2col.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
/*
* \brief Context projection concatenate features in adjacent time steps in
* a sequence. The i-th row of the output is the concatenation of
* context_length rows of the input. The context_length rows are the
* consecutive rows from the i+shift_start row.
* \param in Input data.
* \param Shape The shape of Input data,
* [minibatch, number_of_input_features].
* \param type A float LoDTensor.
*
* \param padding_data Padding data.
* \param Shape The shape of Padding data,
* [up_pad + down_pad, number_of_input_features].
* \param type A float Tensor.
*
* \param col Col data.
* \param Shape The shape of Col data,
* [minibatch, context_length * number_of_input_features].
* \param type A float Tensor.
*
* For a mini-batch of 2 variable lengths sentences, containing 3, and 1
* time-steps:
*
* Assumed input (X) is a [4, M, N] float LoDTensor, and X->lod()[0] = [0, 3,
* 4].
* Besides, for the sake of simplicity, we assume M=1 and N=2.
*
* X = [[a1, a2;
* b1, b2;
* c1, c2]
* [d1, d2]]
*
* This is to say that input (X) has 4 words and the dimension of each word
* representation is 2.
*
* - Case1:
* If context_start is -1 and padding_trainable is false, we use zero to pad
* instead of learned weight to pad,
* and the context_lenth is 3, the output (Out) is:
*
* Out =[[0, 0, a1, a2, b1, b2;
* a1, a2, b1, b2, c1, c2;
* b1, b2, c1, c2, 0, 0 ]
* [0, 0, d1, d2, 0, 0 ]]
*
* - Case2:
* If context_start is -1 and padding_trainable is true, we use learned weight
* to pad,
* and the context_lenth is 3, the output (Out) is:
*
* Out = [[w1, w2, a1, a2, b1, b2;
* a1, a2, b1, b2, c1, c2;
* b1, b2, c1, c2, w3, w4]
* [w1, w2, d1, d2, w3, w4]]
*
*/
template
<
typename
Place
,
typename
T
>
class
ContextProjectFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
LoDTensor
&
in
,
framework
::
Tensor
&
padding_data
,
framework
::
Tensor
&
col
,
bool
padding_trainable
,
int
context_start
,
int
context_length
,
int
context_stride
,
int
up_pad
,
int
down_pad
,
bool
gradient
,
bool
input_grad
,
bool
pad_grad
)
{
auto
lod_level_0
=
in
.
lod
()[
0
];
paddle
::
operators
::
math
::
Im2ColFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kOCF
,
Place
,
float
>
im2col_ocf
;
paddle
::
operators
::
math
::
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kOCF
,
Place
,
float
>
col2im_ocf
;
int
input_row_begin
,
input_row_end
;
int
sequence_height
,
sequence_width
;
sequence_width
=
in
.
dims
()[
1
];
input_grad
=
gradient
&&
input_grad
;
pad_grad
=
gradient
&&
pad_grad
;
if
(
!
gradient
||
input_grad
)
{
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod_level_0
.
size
())
-
1
;
++
i
)
{
input_row_begin
=
(
context_start
>
0
)
?
static_cast
<
int
>
(
lod_level_0
[
i
])
+
context_start
:
static_cast
<
int
>
(
lod_level_0
[
i
]);
input_row_end
=
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]);
framework
::
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
sequence_height
=
static_cast
<
int
>
(
out_t
.
dims
()[
0
]);
if
(
input_row_begin
<
input_row_end
)
{
framework
::
Tensor
in_t
=
in
.
Slice
(
input_row_begin
,
input_row_end
);
std
::
vector
<
int64_t
>
output_shape
(
{
sequence_height
,
1
,
1
,
context_length
,
sequence_width
});
// output_height, output_width,
// input_channels, filter_height, filter_width
out_t
.
Resize
(
framework
::
make_ddim
(
output_shape
));
std
::
vector
<
int64_t
>
input_shape
(
{
1
,
input_row_end
-
input_row_begin
,
sequence_width
});
// input_channels, input_height, input_width
in_t
.
Resize
(
framework
::
make_ddim
(
input_shape
));
if
(
gradient
)
{
col2im_ocf
(
context
,
in_t
,
out_t
,
/*stride_height*/
context_stride
,
/*stride_width*/
1
,
up_pad
,
down_pad
,
0
,
0
);
}
else
{
im2col_ocf
(
context
,
in_t
,
out_t
,
/*stride_height*/
context_stride
,
/*stride_width*/
1
,
up_pad
,
down_pad
,
0
,
0
);
}
out_t
.
Resize
({
sequence_height
,
context_length
*
sequence_width
});
}
}
}
if
(
!
gradient
||
pad_grad
)
{
if
(
padding_trainable
)
{
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod_level_0
.
size
())
-
1
;
++
i
)
{
framework
::
Tensor
out_t
=
col
.
Slice
(
static_cast
<
int
>
(
lod_level_0
[
i
]),
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]));
sequence_height
=
static_cast
<
int
>
(
out_t
.
dims
()[
0
]);
// add up trainable data
out_t
.
Resize
({
sequence_height
*
context_length
,
sequence_width
});
if
(
up_pad
>
0
)
{
// add up pad
int
padding_rows
=
std
::
min
(
up_pad
,
static_cast
<
int
>
(
lod_level_0
[
i
+
1
]
-
lod_level_0
[
i
]));
for
(
int
k
=
0
;
k
<
padding_rows
;
++
k
)
{
int
padding_size
=
k
+
context_length
<
up_pad
?
context_length
:
up_pad
-
k
;
framework
::
Tensor
out_t_sub
=
out_t
.
Slice
(
k
*
context_length
,
k
*
context_length
+
padding_size
);
framework
::
Tensor
w_sub
=
padding_data
.
Slice
(
k
,
k
+
padding_size
);
// in this block, using EigenVector<T>::Flatten is ok too.
auto
out_t_sub_e
=
EigenMatrix
<
T
>::
From
(
out_t_sub
);
auto
w_sub_e
=
EigenMatrix
<
T
>::
From
(
w_sub
);
if
(
gradient
)
{
w_sub_e
.
device
(
*
context
.
GetEigenDevice
<
Place
>
())
=
w_sub_e
+
out_t_sub_e
;
}
else
{
out_t_sub_e
.
device
(
*
context
.
GetEigenDevice
<
Place
>
())
=
w_sub_e
;
}
}
}
if
(
down_pad
>
0
)
{
// add down pad
int
down_pad_begin_row
=
std
::
max
(
0
,
(
sequence_height
-
context_start
-
context_length
)
+
1
)
+
1
;
int
padding_begin
=
std
::
max
(
0
,
context_start
-
sequence_height
);
int
padding_size
=
sequence_height
-
context_start
>=
context_length
?
1
:
context_length
-
(
sequence_height
-
context_start
);
if
(
context_start
>=
sequence_height
)
padding_size
=
context_length
;
int
padding_idx
=
padding_begin
;
for
(
int
t
=
0
;
t
+
down_pad_begin_row
<=
sequence_height
;
++
t
,
++
padding_size
)
{
if
(
context_start
>=
sequence_height
)
padding_size
=
context_length
;
if
(
padding_size
>
context_length
)
{
padding_size
=
context_length
;
padding_idx
++
;
}
if
(
padding_begin
>
0
||
sequence_height
==
context_start
)
padding_idx
=
padding_begin
+
t
;
framework
::
Tensor
out_t_sub
=
out_t
.
Slice
(
(
down_pad_begin_row
+
t
)
*
context_length
-
padding_size
,
(
down_pad_begin_row
+
t
)
*
context_length
);
framework
::
Tensor
w_sub
=
padding_data
.
Slice
(
up_pad
+
padding_idx
,
up_pad
+
padding_idx
+
padding_size
);
auto
out_t_sub_e
=
EigenMatrix
<
T
>::
From
(
out_t_sub
);
auto
w_sub_e
=
EigenMatrix
<
T
>::
From
(
w_sub
);
if
(
gradient
)
{
w_sub_e
.
device
(
*
context
.
GetEigenDevice
<
Place
>
())
=
w_sub_e
+
out_t_sub_e
;
}
else
{
out_t_sub_e
.
device
(
*
context
.
GetEigenDevice
<
Place
>
())
=
w_sub_e
;
}
}
}
out_t
.
Resize
({
sequence_height
,
context_length
*
sequence_width
});
}
}
}
}
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/sequence_conv_op.cc
0 → 100644
浏览文件 @
8e3ecf5d
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/sequence_conv_op.h"
namespace
paddle
{
namespace
operators
{
class
SequenceConvOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceConvOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Filter"
),
"Input(Filter) of SequenceConvOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceConvOp should not be null."
);
int
context_length
=
ctx
->
Attrs
().
Get
<
int
>
(
"context_length"
);
bool
padding_trainable
=
ctx
->
Attrs
().
Get
<
bool
>
(
"padding_trainable"
);
int
context_start
=
ctx
->
Attrs
().
Get
<
int
>
(
"context_start"
);
auto
in_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
filter_dims
=
ctx
->
GetInputDim
(
"Filter"
);
PADDLE_ENFORCE
(
in_dims
.
size
()
==
2
&&
filter_dims
.
size
()
==
2
,
"Input(X, Filter) should be 2-D tensor."
);
PADDLE_ENFORCE
(
filter_dims
[
0
]
==
context_length
*
in_dims
[
1
],
"Filter's height should be context_length * "
"number_of_input_features ."
);
if
(
padding_trainable
)
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"PaddingData"
),
"Input(PaddingData) of SequenceConvOp should not be null."
);
framework
::
DDim
padding_dim
=
ctx
->
GetInputDim
(
"PaddingData"
);
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
int
down_pad
=
std
::
max
(
0
,
context_start
+
context_length
-
1
);
int
total_pad
=
up_pad
+
down_pad
;
int
input_width
=
static_cast
<
int
>
(
in_dims
[
1
]);
if
(
context_start
==
0
&&
context_length
==
1
)
{
PADDLE_THROW
(
"If context_start is 0 and context_length is 1, padding_trainable "
"should be false."
);
}
PADDLE_ENFORCE
(
padding_dim
.
size
()
==
2
,
"Input(PaddingData) should be 2-D tensor."
);
PADDLE_ENFORCE
(
padding_dim
[
0
]
==
total_pad
&&
padding_dim
[
1
]
==
input_width
,
"Input(PaddingData)'s shape is not consistent with 'context_start' "
"and 'context_length'."
);
}
in_dims
[
1
]
=
filter_dims
[
1
];
ctx
->
SetOutputDim
(
"Out"
,
in_dims
);
ctx
->
ShareLoD
(
"X"
,
"Out"
);
}
};
class
SequenceConvGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Gradient of output(Out) should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"The input(X) should not be null."
);
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"padding_trainable"
)
&&
ctx
->
HasOutput
(
framework
::
GradVarName
(
"PaddingData"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"PaddingData"
),
ctx
->
GetInputDim
(
"PaddingData"
));
}
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Filter"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Filter"
),
ctx
->
GetInputDim
(
"Filter"
));
}
}
};
class
SequenceConvOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SequenceConvOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(LoDTensor) the input(X) is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T, D), where, T is the "
"total time steps in this mini-batch, D is the input feature size."
);
AddInput
(
"PaddingData"
,
"(Tensor, optional) the input(PaddingData) is an optional "
"parameter, and it is learnable. "
"This is a tensor with shape (N, D), where N is the "
"top_pad + bottom_pad, D is the input feature size. In order to "
"ensure the equal length of sequence before and after "
"convolution, it is necessary to fill the top and bottom of each "
"sequence according to context_length, context_stride and "
"context_start"
)
.
AsDispensable
();
AddInput
(
"Filter"
,
"(Tensor) the input(Filter) is an learnable parameter."
"This is a tensor with shape (N, D), where N is the "
"context_length, D is the output feature size."
);
AddOutput
(
"Out"
,
"(LoDTensor) the output(Out) is a LodTensor, which support "
"variable-time length output sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T, D), where, T is the "
"total time steps in this mini-batch, D is the output feature size."
);
AddAttr
<
bool
>
(
"padding_trainable"
,
"(bool, default false) the padding data of SequenceConvOp "
"is trainable or not."
)
.
SetDefault
(
false
);
AddAttr
<
int
>
(
"context_length"
,
"(int, default 3) the context_length of SequenceConvOp is the "
"height of the convolution kernel."
)
.
SetDefault
(
3
)
.
GreaterThan
(
0
);
AddAttr
<
int
>
(
"context_start"
,
"(int, default 0) the context_start of SequenceConvOp "
"represents the beginning of the convolution of the number of "
"rows of sequence, which can be negative."
)
.
SetDefault
(
0
);
AddAttr
<
int
>
(
"context_stride"
,
"(int, default 1) the context_stride of SequenceConvOp "
"represents the step length of convolution. "
"Currently, SequenceConvOp only supports"
"context_stride=1."
)
.
SetDefault
(
1
)
.
GreaterThan
(
0
);
AddComment
(
R"DOC(
SequenceConvOp performs convolution operation on features of
context_length time-steps of each instance.
The convolution operation calculates the output based on the input, filter
and strides, paddings parameters. The size of each dimension of the
parameters is checked in the infer-shape. In order to ensure the equal
length of sequence before and after convolution, it is necessary to fill
the top and bottom of each sequence according to context_length,
context_stride and context_start.
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
sequence_conv
,
ops
::
SequenceConvOp
,
ops
::
SequenceConvOpMaker
,
sequence_conv_grad
,
ops
::
SequenceConvGradOp
);
REGISTER_OP_CPU_KERNEL
(
sequence_conv
,
ops
::
SequenceConvKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_conv_grad
,
ops
::
SequenceConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/sequence_conv_op.cu
0 → 100644
浏览文件 @
8e3ecf5d
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/sequence_conv_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
sequence_conv
,
ops
::
SequenceConvKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
sequence_conv_grad
,
ops
::
SequenceConvGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/sequence_conv_op.h
0 → 100644
浏览文件 @
8e3ecf5d
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/context_project.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
Place
,
typename
T
>
class
SequenceConvKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
auto
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
context
.
ShareLoD
(
"X"
,
"Out"
);
int
context_start
=
context
.
Attr
<
int
>
(
"context_start"
);
int
context_length
=
context
.
Attr
<
int
>
(
"context_length"
);
int
context_stride
=
context
.
Attr
<
int
>
(
"context_stride"
);
bool
padding_trainable
=
context
.
Attr
<
bool
>
(
"padding_trainable"
);
// InferShape by in_lod
PADDLE_ENFORCE_EQ
(
in
->
lod
().
size
(),
1UL
,
"Only support one level sequence now."
);
const
Tensor
*
padding_data
=
nullptr
;
if
(
padding_trainable
)
{
padding_data
=
context
.
Input
<
Tensor
>
(
"PaddingData"
);
}
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
int
down_pad
=
std
::
max
(
0
,
context_start
+
context_length
-
1
);
int
sequence_width
;
sequence_width
=
static_cast
<
int
>
(
in
->
dims
()[
1
]);
// Use col_shape in the im2col calculation.
framework
::
DDim
col_shape
=
{
in
->
dims
()[
0
],
sequence_width
*
context_length
};
Tensor
col
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
math
::
SetConstant
<
Place
,
T
>
set_zero
;
// Because if padding_trainable is false, padding data should be zeros.
set_zero
(
context
.
device_context
(),
&
col
,
static_cast
<
T
>
(
0
));
paddle
::
operators
::
math
::
ContextProjectFunctor
<
Place
,
T
>
seq_project_functor
;
LoDTensor
*
input
=
const_cast
<
LoDTensor
*>
(
in
);
Tensor
*
pad_data
=
const_cast
<
Tensor
*>
(
padding_data
);
seq_project_functor
(
context
.
device_context
(),
*
input
,
*
pad_data
,
col
,
padding_trainable
,
context_start
,
context_length
,
context_stride
,
up_pad
,
down_pad
,
false
,
false
,
false
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
col
,
false
,
filter
,
false
,
static_cast
<
T
>
(
1.0
),
out
,
static_cast
<
T
>
(
0.0
));
}
};
template
<
typename
Place
,
typename
T
>
class
SequenceConvGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
in_g
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
filter_g
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
auto
*
padding_data_g
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"PaddingData"
));
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
filter
=
context
.
Input
<
Tensor
>
(
"Filter"
);
int
context_start
=
context
.
Attr
<
int
>
(
"context_start"
);
int
context_length
=
context
.
Attr
<
int
>
(
"context_length"
);
int
context_stride
=
context
.
Attr
<
int
>
(
"context_stride"
);
bool
padding_trainable
=
context
.
Attr
<
bool
>
(
"padding_trainable"
);
PADDLE_ENFORCE_EQ
(
in
->
lod
().
size
(),
1UL
,
"Only support one level sequence now."
);
auto
lod_g_level_0
=
in
->
lod
()[
0
];
int
up_pad
=
std
::
max
(
0
,
-
context_start
);
int
down_pad
=
std
::
max
(
0
,
context_start
+
context_length
-
1
);
int
sequence_width
=
static_cast
<
int
>
(
in
->
dims
()[
1
]);
math
::
SetConstant
<
Place
,
T
>
set_zero
;
// use col_shape in the im2col calculation
framework
::
DDim
col_shape
=
{
in
->
dims
()[
0
],
sequence_width
*
context_length
};
Tensor
col
;
if
(
in_g
||
filter_g
||
(
padding_trainable
&&
padding_data_g
))
{
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
// Because if padding_trainable is false, padding data should be zeros.
set_zero
(
context
.
device_context
(),
&
col
,
static_cast
<
T
>
(
0
));
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
*
out_g
,
false
,
*
filter
,
true
,
T
(
1.0
),
&
col
,
T
(
1.0
));
}
paddle
::
operators
::
math
::
ContextProjectFunctor
<
Place
,
T
>
seq_project_functor
;
if
(
in_g
)
{
in_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
in_g
->
set_lod
(
in
->
lod
());
set_zero
(
context
.
device_context
(),
in_g
,
static_cast
<
T
>
(
0
));
seq_project_functor
(
context
.
device_context
(),
*
in_g
,
*
padding_data_g
,
col
,
padding_trainable
,
context_start
,
context_length
,
context_stride
,
up_pad
,
down_pad
,
true
,
true
,
false
);
}
if
(
padding_trainable
&&
padding_data_g
)
{
padding_data_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
set_zero
(
context
.
device_context
(),
padding_data_g
,
static_cast
<
T
>
(
0
));
LoDTensor
*
input
=
const_cast
<
LoDTensor
*>
(
in
);
seq_project_functor
(
context
.
device_context
(),
*
input
,
*
padding_data_g
,
col
,
padding_trainable
,
context_start
,
context_length
,
context_stride
,
up_pad
,
down_pad
,
true
,
false
,
true
);
}
if
(
filter_g
)
{
filter_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
set_zero
(
context
.
device_context
(),
filter_g
,
static_cast
<
T
>
(
0
));
Tensor
filter_grad
=
*
filter_g
;
LoDTensor
out_grad
=
*
out_g
;
const
Tensor
*
padding_data
=
nullptr
;
if
(
padding_trainable
)
{
padding_data
=
context
.
Input
<
Tensor
>
(
"PaddingData"
);
}
sequence_width
=
static_cast
<
int
>
(
in
->
dims
()[
1
]);
LoDTensor
*
input
=
const_cast
<
LoDTensor
*>
(
in
);
Tensor
*
pad_data
=
const_cast
<
Tensor
*>
(
padding_data
);
seq_project_functor
(
context
.
device_context
(),
*
input
,
*
pad_data
,
col
,
padding_trainable
,
context_start
,
context_length
,
context_stride
,
up_pad
,
down_pad
,
false
,
false
,
false
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
col
,
true
,
out_grad
,
false
,
T
(
1.0
),
&
filter_grad
,
T
(
1.0
));
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_seq_conv.py
0 → 100644
浏览文件 @
8e3ecf5d
import
unittest
import
numpy
as
np
import
random
from
op_test
import
OpTest
class
TestSeqProject
(
OpTest
):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
op_type
=
'sequence_conv'
if
self
.
context_length
==
1
\
and
self
.
context_start
==
0
\
and
self
.
padding_trainable
:
print
"If context_start is 0 "
\
"and context_length is 1,"
\
" padding_trainable should be false."
return
# one level, batch size
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
input_size
[
0
],
self
.
input_size
[
1
]]).
astype
(
'float32'
)
w
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
context_length
*
self
.
input_size
[
1
],
self
.
output_represention
]).
astype
(
'float32'
)
begin_pad
=
np
.
max
([
0
,
-
self
.
context_start
])
end_pad
=
np
.
max
([
0
,
self
.
context_start
+
self
.
context_length
-
1
])
total_pad
=
begin_pad
+
end_pad
padding_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
total_pad
,
self
.
input_size
[
1
]]).
astype
(
'float32'
)
self
.
pad_data
=
padding_data
self
.
inputs
=
{
'X'
:
(
x
,
self
.
lod
),
'Filter'
:
w
,
}
self
.
inputs_val
=
[
'X'
,
'Filter'
]
self
.
inputs_val_no_x
=
[
'Filter'
]
self
.
inputs_val_no_f
=
[
'X'
]
if
total_pad
!=
0
:
self
.
inputs
[
'PaddingData'
]
=
padding_data
self
.
inputs_val
=
[
'X'
,
'PaddingData'
,
'Filter'
]
self
.
inputs_val_no_x
=
[
'PaddingData'
,
'Filter'
]
self
.
inputs_val_no_f
=
[
'PaddingData'
,
'X'
]
self
.
attrs
=
{
'context_start'
:
self
.
context_start
,
'context_length'
:
self
.
context_length
,
'padding_trainable'
:
self
.
padding_trainable
,
'context_stride'
:
self
.
context_stride
}
out
=
np
.
zeros
(
(
self
.
input_size
[
0
],
self
.
output_represention
)).
astype
(
'float32'
)
self
.
outputs
=
{
'Out'
:
out
}
self
.
compute
()
def
compute
(
self
):
x
,
lod
=
self
.
inputs
[
'X'
]
filter
=
self
.
inputs
[
'Filter'
]
pading_data
=
self
.
pad_data
out
=
np
.
zeros
((
self
.
input_size
[
0
],
self
.
context_length
*
self
.
input_size
[
1
])).
astype
(
'float32'
)
lod
=
lod
[
0
]
begin_pad
=
np
.
max
([
0
,
-
self
.
context_start
])
for
i
in
range
(
len
(
lod
)
-
1
):
for
j
in
range
(
self
.
context_length
):
in_begin
=
lod
[
i
]
+
self
.
context_start
+
j
in_end
=
lod
[
i
+
1
]
+
self
.
context_start
+
j
out_begin
=
lod
[
i
]
out_end
=
lod
[
i
+
1
]
if
in_begin
<
lod
[
i
]:
pad_size
=
np
.
min
([
lod
[
i
]
-
in_begin
,
lod
[
i
+
1
]
-
lod
[
i
]])
if
self
.
padding_trainable
:
sub_w
=
pading_data
[
j
:
j
+
pad_size
,
:]
out
[
lod
[
i
]:
lod
[
i
]
+
pad_size
,
j
*
self
.
input_size
[
1
]:(
j
+
1
)
*
self
.
input_size
[
1
]]
=
sub_w
out_begin
=
lod
[
i
]
+
pad_size
in_begin
=
lod
[
i
]
if
in_end
>
lod
[
i
+
1
]:
pad_size
=
np
.
min
(
[
in_end
-
lod
[
i
+
1
],
lod
[
i
+
1
]
-
lod
[
i
]])
if
self
.
padding_trainable
:
sub_w
=
pading_data
[
begin_pad
+
self
.
context_start
+
j
-
pad_size
:
begin_pad
+
self
.
context_start
+
j
,
:]
out
[
lod
[
i
+
1
]
-
pad_size
:
lod
[
i
+
1
],
j
*
self
.
input_size
[
1
]:(
j
+
1
)
*
self
.
input_size
[
1
]]
=
sub_w
in_end
=
lod
[
i
+
1
]
out_end
=
lod
[
i
+
1
]
-
pad_size
if
in_end
<=
in_begin
:
continue
in_sub
=
x
[
in_begin
:
in_end
,
:]
out
[
out_begin
:
out_end
,
j
*
self
.
input_size
[
1
]:(
j
+
1
)
*
self
.
input_size
[
1
]]
+=
in_sub
np
.
dot
(
out
,
filter
,
out
=
self
.
outputs
[
'Out'
])
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
set
(
self
.
inputs_val
),
'Out'
,
max_relative_error
=
0.05
)
def
test_check_grad_input
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
(
self
.
inputs_val_no_x
))
def
test_check_grad_padding_data
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
[
'PaddingData'
],
'Out'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'X'
,
'Filter'
]))
def
test_check_grad_Filter
(
self
):
self
.
check_grad
(
[
'Filter'
],
'Out'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
(
self
.
inputs_val_no_f
))
def
test_check_grad_input_filter
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
[
'X'
,
'Filter'
],
'Out'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'PaddingData'
]))
def
test_check_grad_padding_input
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
self
.
inputs_val_no_f
,
'Out'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'Filter'
]))
def
test_check_grad_padding_filter
(
self
):
if
self
.
padding_trainable
:
self
.
check_grad
(
self
.
inputs_val_no_x
,
'Out'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'X'
]))
def
init_test_case
(
self
):
self
.
input_row
=
11
self
.
context_start
=
0
self
.
context_length
=
1
self
.
padding_trainable
=
False
self
.
context_stride
=
1
self
.
input_size
=
[
self
.
input_row
,
23
]
self
.
lod
=
[[
0
,
4
,
5
,
8
,
self
.
input_row
]]
self
.
output_represention
=
8
# output feature size
class
TestSeqProjectCase1
(
TestSeqProject
):
def
init_test_case
(
self
):
self
.
input_row
=
11
self
.
context_start
=
-
1
self
.
context_length
=
3
self
.
padding_trainable
=
True
self
.
context_stride
=
1
self
.
input_size
=
[
self
.
input_row
,
23
]
self
.
lod
=
[[
0
,
4
,
5
,
8
,
self
.
input_row
]]
self
.
output_represention
=
8
# output feature size
class
TestSeqProjectCase2
(
TestSeqProject
):
def
init_test_case
(
self
):
self
.
input_row
=
25
self
.
context_start
=
2
self
.
context_length
=
3
self
.
padding_trainable
=
True
self
.
context_stride
=
1
self
.
input_size
=
[
self
.
input_row
,
23
]
idx
=
range
(
self
.
input_size
[
0
])
del
idx
[
0
]
self
.
lod
=
[[
0
]
+
np
.
sort
(
random
.
sample
(
idx
,
8
)).
tolist
()
+
[
self
.
input_size
[
0
]]]
self
.
output_represention
=
8
# output feature size
if
__name__
==
'__main__'
:
unittest
.
main
()
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